code: def model(opt):
      model = GradientBoostingClassifier(
          n_estimators=opt["n_estimators"],
          max_depth=opt["max_depth"],
      )
  
      X_pca = opt["decomposition"](X, opt)
      X_mod = np.hstack((X, X_pca))
  
      X_best = SelectKBest(f_classif, k=opt["k"]).fit_transform(
          X_mod, y
      )
      scores = cross_val_score(model, X_best, y, cv=3)
  
      return scores.mean()
  
file_path: Hyperactive/examples/optimization_applications/sklearn_preprocessing.py
imports: import numpy
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import cross_val_score
  from sklearn.decomposition import PCA
  from sklearn.feature_selection import SelectKBest, f_classif
  from sklearn.ensemble import GradientBoostingClassifier
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: model(opt) and returns: scores